# Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition

arXiv: https://arxiv.org/abs/2505.01783 · HTML: https://mlsanity.com/paper/2505.01783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 22 of 22)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Testing for Outliers with Conformal p-values](https://mlsanity.com/paper/2104.08279/) (2021)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [Conformal Prediction with Missing Values](https://mlsanity.com/paper/2306.02732/) (2023)
- [Online Rules for Control of False Discovery Rate and False Discovery Exceedance](https://mlsanity.com/paper/1603.09000/) (2016)
- [Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation](https://mlsanity.com/paper/2010.05119/) (2020)
- [5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network](https://mlsanity.com/paper/2212.01298/) (2022)
- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [Online false discovery rate control for anomaly detection in time series](https://mlsanity.com/paper/2112.03196/) (2021)
- [Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data](https://mlsanity.com/paper/2105.11886/) (2021)
- [Robust Conformal Outlier Detection under Contaminated Reference Data](https://mlsanity.com/paper/2502.04807/) (2025)
- [Online control of the false discovery rate with decaying memory](https://mlsanity.com/paper/1710.00499/) (2017)
- [SAFFRON: an adaptive algorithm for online control of the false discovery rate](https://mlsanity.com/paper/1802.09098/) (2018)
- [ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls](https://mlsanity.com/paper/1905.11465/) (2019)
- [Conflict Mitigation Framework and Conflict Detection in O-RAN Near-RT RIC](https://mlsanity.com/paper/2305.07117/) (2023)
- [Variational Autoencoder for Anomaly Detection: A Comparative Study](https://mlsanity.com/paper/2408.13561/) (2024)
- [Toward Digital Network Twins: Integrating Sionna RT in ns-3 for 6G Multi-RAT Networks Simulations](https://mlsanity.com/paper/2501.00372/) (2024)
- [O-RAN Xapps Conflict Prediction Using Graph Convolutional Networks](https://mlsanity.com/paper/2503.03523/) (2025)
- [Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies](https://mlsanity.com/paper/2409.10069/) (2024)
- [Active multiple testing with proxy p-values and e-values](https://mlsanity.com/paper/2502.05715/) (2025)
- [OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data](https://mlsanity.com/paper/2409.09742/) (2024)

## Abstract (arXiv)

Online anomaly detection is essential in fields such as cybersecurity, healthcare, industrial monitoring, and telecommunications, where promptly identifying deviations from expected behavior can avert critical failures or security breaches. While numerous anomaly scoring methods based on supervised or unsupervised learning have been proposed, the only existing approach capable of providing assumption-free guarantees on the false discovery rate (FDR) rely on a continuous stream of real-world calibration data. To address this limitation, we introduce context-aware prediction-powered conformal online anomaly detection (C-PP-COAD), a novel principled framework that strategically leverages synthetic calibration data to mitigate data scarcity, while adaptively integrating real data based on contextual information. C-PP-COAD wraps around any existing anomaly detection method, leveraging any given anomaly score to construct active conformal p-value statistics. These statistics support online testing with formal FDR control, maintaining rigorous and reliable anomaly detection performance over time. Experiments conducted on both synthetic and real-world datasets, including thyroid dysfunction detection, O-RAN conflict detection, 5G network intrusion detection, and O-RAN UE throughput degradation detection, demonstrate that C-PP-COAD significantly reduces dependency on real calibration data without compromising guaranteed FDR control.